I have a meta-analysis dataset which I would like to analyze as a mixed model, where the y-variable is a measure of effect size, the random effect is the study from which the effect size was extracted, and the fixed effect is a categorical explanatory variable. The complication is that we often have multiple estimates of effect size from a single study (e.g. the experiment was repeated in different years, or under different conditions). Being a meta-analysis, I need to weight the effect sizes by the inverse of the effect SE. Thus my dataset includes: study, effect size, SE, explanatory variables.
The problem is that I have failed to find a mixed model package which allows me to both weight the y-variable and include study as a random effect. Specifically, the linear mixed model packages (e.g. nlme) allow for random effects but not weighting of the y (the "weight" command of these functions means something quite different!). The rmeta package allows for both weighting and random effects, but is only for binary data. The mima function of Wolfgang Viechtbauer allows both weighting and random effects but assumes that each row of data is from a separate study (not true in my dataset). Any help would be appreciated, as I seem to have hit a dead end. Diane Srivastava -- D.S. Srivastava Associate Professor Dept. of Zoology University of British Columbia 6270 University Blvd. Vancouver B.C. V6T 1Z4 Canada [EMAIL PROTECTED] www.zoology.ubc.ca/~srivast/ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
